The economic costs of a multisectoral nutrition programme implemented through a credit platform in Bangladesh

Abstract Bangladesh struggles with undernutrition in women and young children. Nutrition‐sensitive agriculture programmes can help address rural undernutrition. However, questions remain on the costs of multisectoral programmes. This study estimates the economic costs of the Targeting and Re‐aligning Agriculture to Improve Nutrition (TRAIN) programme, which integrated nutrition behaviour change and agricultural extension with a credit platform to support women's income generation. We used the Strengthening Economic Evaluation for Multisectoral Strategies for Nutrition (SEEMS‐Nutrition) approach. The approach aligns costs with a multisectoral nutrition typology, identifying inputs and costs along programme impact pathways. We measure and allocate costs for activities and inputs, combining expenditures and micro‐costing. Quantitative and qualitative data were collected retrospectively from implementers and beneficiaries. Expenditure data and economic costs were combined to calculate incremental economic costs. The intervention was designed around a randomised control trial. Incremental costs are presented by treatment arm. The total incremental cost was $795,040.34 for a 3.5‐year period. The annual incremental costs per household were US$65.37 (Arm 2), USD$114.15 (Arm 3) and $157.11 (Arm 4). Total costs were led by nutrition counselling (37%), agriculture extension (12%), supervision (12%), training (12%), monitoring and evaluation (9%) and community events (5%). Total input costs were led by personnel (68%), travel (12%) and supplies (7%). This study presents the total incremental costs of an agriculture‐nutrition intervention implemented through a microcredit platform. Costs per household compare favourably with similar interventions. Our results illustrate the value of a standardised costing approach for comparison with other multisectoral nutrition interventions.


| BACKGROUND
Child and maternal malnutrition is a persistent problem in Bangladesh. The prevalence of stunting in Bangladesh is 31% for children under 5, with 9% severely stunted and 2% severely wasted. Further, children in rural areas of the country are more likely to be stunted than their counterparts in urban areas. In Bangladesh, 24% of evermarried women between the ages of 15−19 years of age are undernourished and rural women are more likely to suffer from undernourishment than urban women. At the same time, the proportion of overweight women has increased to 32%, highlighting the problem of both under and overnutrition in the country (NIPORT & ICF, 2020).
Nutrition-specific interventions that address the immediate causes of malnutrition have long been used to target undernutrition.
Nutrition-sensitive interventions-that address intermediate or underlying causes of malnutrition and address multiple outcomes-have also shown promise. Nutrition-sensitive agriculture (NSA) programmes, in particular, have the potential to accelerate progress in addressing child malnutrition (Ruel & Alderman, 2013). Effective NSA programmes often combine behaviour change communication (BCC) with the production of nutrient-rich foods, increasing dietary diversity and improving caregiver knowledge and infant and young child feeding practices in poor households (Keats et al., 2021).
Engaging women in agriculture and nutrition can increase their decision-making power and control over assets, albeit with potential tradeoffs in work burdens. Women's empowerment in Bangladesh has also been linked to improvements in diet diversity and reductions in child stunting (Holland & Rammohan, 2019). Growing evidence shows that well-implemented nutrition-sensitive agricultural programmes can improve maternal and child diets and household access to nutritious foods (Ruel et al., 2018). However, a key challenge for policymakers and programme implementers prioritising investment decisions hinges on the gap in the evidence on the costs of implementing multisectoral agriculture-nutrition programmes (Ruel & Alderman, 2013;Ruel et al., 2018). This is further limited by a lack of standardised methods that make comparisons difficult to interpret (Gyles et al., 2012;Njuguna et al., 2020;Ramponi et al., 2021).
The Targeting and Realigning Agriculture to Improve Nutrition (TRAIN) project was a multisectoral nutrition-sensitive intervention This study presents the incremental costs of implementing an integrated agriculture-nutrition intervention through an existing BRAC microcredit platform. 1 We estimate the total incremental financial and economic costs of the TRAIN programme by implementation arm, including costs per beneficiary. We also examine cost shares by programme inputs and activities.
Financial costs represent the implementing partner's actual expenditures on goods and services purchased to deploy the intervention.
Economic costs, on the other hand, are defined as the opportunity cost of all of the resources used to produce something; and can include the value of resources that may not have been paid for, such as volunteer frontline worker time or programme participant time. Once data becomes available from an ongoing impact evaluation, these costs will be combined with programme benefits for a full economic evaluation including cost-benefit and cost-effectiveness analyses.
Our findings serve many purposes. A robust understanding of the costs of multisectoral nutrition strategies is critical for priority-setting and for motivating donors. This study is useful for governments and development partners to target investments in multisectoral nutrition programmes. Standardised unit cost data provides a cost benchmark for governments, donors and non-profit organisations on how to design, budget and measure the resource requirements of interventions. Lastly, this analysis contributes to an effort to build an evidence base on the costs of multisectoral nutrition programmes across different settings and platforms. This study was conducted by the Strengthening Economic Evaluation for Multisectoral Strategies for Nutrition (SEEMS-Nutrition) consortium, led by the University of Washington in partnership with IFPRI. SEEMS-Nutrition develops standardised approaches and tools to assess the costs and benefits of multisectoral nutrition programmes.

Key messages
• Nutrition-sensitive agriculture programmes can improve rural undernutrition but lack information on costs.
• We use a standardised approach to estimate the total incremental costs of an integrated nutrition intervention in Bangladesh to improve maternal and child undernutrition.
• Costs per household compare favourably with similar interventions.
• This study provides evidence on the costs of integration to support the design and implementation of multisectoral nutrition programmes.
• Our results illustrate the value of standardising costing to facilitate comparisons with other multisectoral nutrition interventions. 1 Microcredit is a financial service offered by microfinance programmes, which target the poor and others unable to access traditional banks.
were selected from 144 unions of 36 Subdistricts from 10 districts.
TRAIN incorporated a BCC strategy for maternal and child health and nutrition into a female-focused microcredit programme promoting production diversity and income generation.
BRAC has led microcredit programmes in Bangladesh since 1974.
Dabi, a credit platform lending only to women, has coverage through 2146 local branches with more than 3.5 million borrowers. Dabi provides loans to women to increase income and production in agriculture and to promote empowerment. Dabi disburses $1.8 billion in loans annually, the majority for agriculture (60%). 2 The TRAIN programme was built upon the existing Dabi microcredit platform linked to agriculture. Programme criteria dictate that one member of the household is a married female Dabi beneficiary (henceforth referred to as the 'index female') of childbearing age between 15 and 49 years old. If there was more than one woman with qualifying criteria in the household, then one women was selected randomly. Given that the last 3 months of implementation were disrupted by COVID-19 and extended until October 2020, our study only covers the pre-COVID period.

| METHODS
This study used a novel standardised costing approach that contributes to gaps in the literature on the costs of multisectoral nutrition programmes (Margolies et al., 2021). The methodology was developed by SEEMS-Nutrition, led by the University of Washington in collaboration with IFPRI and funded by the Bill and Melinda Gates Foundation. The SEEMS-Nutrition approach provides standardised research protocols, data collection tools and guidance on allocating costs. This approach defines a set of input and activity cost category codes that are specific to multisectoral intervention components for agriculture, nutrition and gender empowerment (C. ).
Our analysis also adheres to principles outlined in the Global Health Cost Consortium Reference Case for Estimating the Costs of Global Health Services and Interventions (Vassall et al., 2017).
The cost analysis was conducted from the payer and societal perspectives. The analysis included costs incurred by BRAC, frontline workers and programme beneficiaries. Costs related to third-party external research were excluded. The SEEMS-Nutrition framework uses a four-step approach. Costs are aligned with a multisectoral nutrition typology that identifies resource use and outputs along the programme impact pathways to achieve standardised unit costs and the basis for benchmarking and economic evaluation.
Step 1 aligns the TRAIN programme to a typology of nutrition-sensitive value (NSV) chain interventions that (1) increase the supply of nutrient-rich foods, (2) increase the demand for nutrient-rich foods, and (3) promote the enabling environment for nutrition.
Step 2 maps the programme impact pathways to clearly articulate the linkages from activities to outputs and outcomes.
Step 3 identifies all activities, inputs and costs along the impact pathway.
Step 4 identifies outputs and outcomes for each activity to define the components of total and unit costs. The four-step framework estimates the direct intervention costs and opportunity costs associated with all programme activities.
Multisectoral nutrition approaches may include one or more of the intervention typologies with different components, services and outputs. Therefore, it is important to clearly define the unit cost for one or more outputs. Supporting Information: Appendix Table 1 shows the standardised process to derive the unit cost per beneficiary. Supporting Information: Appendix Tables 2 and 3 describe the standardised SEEMS-Nutrition activity and input categories and definitions. Supporting Information: Appendix Table 4 illustrates how costs were mapped to the NSV chain typology.

| Data collection
The cost analysis captures the total costs of the TRAIN intervention incremental to the existing microcredit programme. Primary and secondary cost data were collected for the period of October 2016−January 2020, which included 6 months of start-up and 3 years of full implementation. The SEEMS mixed-methods approach 2 BRAC. http://www.brac.net/sites/default/files/microfinance.pdf. THAI ET AL. | 3 of 12 combines financial expenditure data with micro-costing methods to identify and value resources and allocate costs to activities and inputs. The Activity-Based-Costing-Ingredients (ABC-I) method (Kaplan & Anderson, 2004;Tan-Torres Edejer et al., 2003) for micro-costing has been previously applied to nutrition programmes to assess cost-efficiency and cost-effectiveness (Fiedler et al., 2008;Heckert et al., 2019;Margolies & Hoddinott, 2015). Costs were obtained from existing records, surveys and from primary data collection with BRAC.
We leveraged secondary data from programme monitoring and reports. When available, planning and progress reports were reviewed retrospectively for randomly selected programme staff and frontline workers. Costs were disaggregated into start-up and recurrent categories. Start-up costs occurred in the first 6 months, such as planning, materials development and staff training. Recurrent costs included ongoing activities like household visits, community events and monitoring.
Primary cost data were collected in two rounds (May 2019 and February 2020) through semistructured in-depth interviews (IDIs) and focus group discussions (FGDs). IDIs and FGDs gathered data on interviewees' opportunity costs and outof-pocket (OPP) expenses. In the first round of data collection, we organised IDIs and FGDs at a centralised location. FGDs were conducted with FOs (n = 1) and DMs (n = 1), and IDIs with BRAC staff (n = 3) and PKs (n = 3). In the second round, 7 additional FGDs were conducted at BRAC regional offices in Rangpur (n = 4), Dhaka (n = 2), and Khulna (n = 4) divisions with PKs for a total of 24 participants. One FGD was conducted with FOs (n = 2) and one FGD was conducted with DMs (n = 5). Additional IDIs were conducted with BRAC head office staff (n = 4). A local research collaborator facilitated and translated interviews in the local language. Supporting Information: Appendix Table 7 provides further primary data collection details.
We estimated the opportunity cost of beneficiary participation with data from the process evaluation. The process evaluation included information on beneficiary time use for the index female respondent and the index husband in each household. 3 The process evaluation was conducted by IFPRI in April 2019. Programme output data were collected from the RCT baseline survey and from BRAC monitoring surveys. These included the number of beneficiaries reached by each activity. These data provided the denominator for the unit cost calculations.

| Data analysis
We analysed secondary expenditure and process evaluation data and combined these with primary data on economic costs. First, we analysed process evaluation data on beneficiary time allocation and OOP expenditures for participating households using Stata 16 statistical software. Second, we obtained financial expenditure data from BRAC. These data were entered into a SEEMS-Nutrition expenditure analysis template in Microsoft Excel (Version 16). We then mapped line-item expenditures to standardised input and activity codes using the template. Third, we used Excel to summarise and analyse micro-costing data from the qualitative interviews and focus group discussion.
Most line-item expenditures were easily mapped to standardised SEEMS activity and input codes. However, there were some exceptions: (1) BRAC personnel who contributed to multiple activities; and (2) shared capital and supply costs. For the first, we developed allocation rules for each activity using data from KIIs and FGDs. For example, using qualitative interviews we found PKs spent 80% of their time on nutrition counselling, 10% on training, 5% on planning and 5% on coordination meetings. PK salaries were allocated accordingly to those activities. Shared inputs or capital costs were allocated proportionally across the related activities.
Shared costs are described in greater detail below.

| Personnel costs
First, all nonshared financial and economic personnel costs were allocated across programme activities. We combined expenditure and time allocation data from BRAC staff and frontline workers (IDIs and FGDs). This information was used to allocate personnel costs to programme activities (Supporting Information: Appendix Table 2). Since the intervention was built upon ongoing activities, only one national staff-a Senior Sector Specialistwas assigned to the programme full-time. We interviewed this person to obtain information on their time allocation to programme activities. At the subnational level, BRAC staff were assigned part-time. The index husband is the spouse of the primary female respondent.

| Economic costs of frontline workers
Estimates of frontline worker (PK) costs include personnel costs from BRAC expenditures combined with estimates of OOP costs and the valuation of time above the contracted 36-h week. Economic costs such as OOPs and overtime hours/travel were not reimbursed by BRAC. PK personnel costs, gleaned from both financial expenditure and economic cost data were allocated based on time spent on programme activities.

| Beneficiary opportunity costs
Beneficiaries participated in programme activities such as household counselling and community events. We estimated the opportunity costs of participation in TRAIN activities from process evaluation data. Opportunity costs were based on information on beneficiaries' OOP expenses and the average time per year spent on programme activities. To value beneficiaries' time accurately, we used daily wage rates for agricultural labour for men and women from a 2017 IFPRI survey in implementation villages. We used mean daily wage rates for male and female agricultural workers to value beneficiaries' time.
Once we obtained all personnel and beneficiary costs aligned to programme activities, we mapped them onto the standardised SEEMS activity categories (Supporting Information: Appendix Table 2).

| Start-up and capital costs
One-time start-up costs, capital and equipment costs for durable goods valued over USD$100 and lasting over 1 year were annuitized. These costs were annuitized over the implementation period using a discount rate of 3% and an expected useful life of 10 years.
Annuitization ensures an equivalent annual cost is estimated and reflects the value-in-use of capital items, rather than reflecting the financial cost from the time of purchase (Brooker et al., 2008). Taxes for durable goods and value-added taxes for small goods where tax was included as part of the commodity cost were included in the financial costs. Taxes were excluded in economic costs except in the case of small supplies. Costs were adjusted for inflation and are presented in 2019 USD using an exchange rate of $1USD/84.77 Bangladesh taka (BDT). 4

| Unit costs
Total incremental costs were broken down by their financial and economic components. The total incremental cost per beneficiary is defined as the total cost divided by the total number of beneficiaries.
We also present cost breakdowns by intervention typology, programme activity, inputs and timing (start-up and recurrent). We estimate annual cost per beneficiary and the annual cost per household by treatment arm. The cost profile is the share of the disaggregated cost over the total programme costs for the 3.5-year period.  (Table 3). We used a gamma distribution for all parameters since hours worked and costs are all nonnegative (Dodd et al, 2006). For each scenario, 5,000 simulations were run with samples drawn from the various parameter distributions. We provide the below rationale and details for each parameter we varied in the sensitivity analysis. See Supporting Information: Appendix Table 6 for additional details.  (Table 3).

| RESULTS
The total incremental cost of the TRAIN programme including economic and financial costs over 3.5 years was USD$795,040.34 (

| Sensitivity analyses
The tornado diagram shown in Figure 3 shows the impact that varying inputs has on the results.

| DISCUSSION
Integrated agriculture and nutrition interventions can provide effective platforms to reach vulnerable populations (Ruel et al., 2018). Despite progress in generating evidence on the effectiveness of these programmes, important gaps remain on intervention costs.
This study offers evidence of the total incremental costs and costs per beneficiary of delivering a multisectoral agriculture-nutrition programme through a microcredit platform in Bangladesh.
Microfinance programmes have been lauded for expanding access to financial services to the poor but have also generated mixed results (Amin et al., 2003;Banerjee et al., 2015). That said, the reach of microfinance programmes-BRAC reaches 126 million people-presents a promising delivery platform for other services targeted to the vulnerable, such as agricultural extension and nutrition behaviour communication change. This study provides insights into the potential costs of such integrated programmes using a microfinance platform including granular details of disaggregated costs including the total incremental financial and economic costs by implementation arm and programme cost drivers by input and activity type.
For intervention unit costs, the average incremental cost per household was USD$63.10 regardless of the treatment arm. Similar to T A B L E 2 Summary of unit costs for the TRAIN intervention in Bangladesh (USD, 2019)

A. Number of beneficiaries
Type of beneficiary  We also examine the costs of incorporating women's empowerment activities. Women's empowerment activities accounted for 30% of programme costs, as part of facilitating the enabling environment for nutrition. As noted, the unit cost per household increased with programme complexity. Thus, Arm 4, which has the most activities, including gender forums and men's sensitisation, had the highest unit costs per beneficiary. Women spent almost twice the amount of time than men; our sensitivity analysis emphasises the range of higher opportunity costs for women (Table 3). This raises the concern of the time burden of programmes targeted at women.  Brauw et al., 2018). A study in Cambodia estimated the costs over ten years of a homestead production intervention at USD$929 per household (Dragojlovic et al., 2020). In Zimbabwe, the costs per household for a programme providing community gardens for people with HIV were USD$1890 (Puett et al., 2014). A crosscountry study in Ethiopia, Nigeria and India modelled costs per child reached for 12 agriculture-nutrition interventions. Modelled costs ranged widely from USD$0.58 for a media and education campaign to USD$2650 for a livestock programme (Masters et al. 2018). While these studies assess costs for different types of interventions and outputs (i.e., individual beneficiaries or households reached), the unit costs generated are critical for decisionmakers to assess the affordability of multisectoral nutrition for a given intervention and country context. They also demonstrate the need for improved guidance to generate standardised cost estimates to increase comparability and generalisability.
Incorporating frontline worker opportunity costs in the TRAIN cost analysis highlights the programme's sustainability concerns.
Despite the travel stipend provided in the second year, frontline workers shouldered additional OOP costs given the intensive nature of household-level interventions. SEEMS-Nutrition is underlining such questions in building evidence on the costs and costeffectiveness of nutrition-sensitive interventions. This study presents full programme costs in the pre-COVID period and is an important step toward a comprehensive economic evaluation. As noted above, a recent systematic review has found that nutrition-sensitive agricultural interventions have a significant positive impact on dietary diversity among children 6−60 months old (Margolies et al. 2022).
This paper helps illustrate the costs of these complex interventions, providing a benchmark that can be used to assess costs and affordability of this and similar programmes. Importantly, the methods outlined here provide a template for future cost analyses and the results provide the groundwork for meaningful comparisons among multisectoral programmes. Lastly, the economic costs, which include both the financial and economic costs of all implementing partners, government and participants, can provide insights into the sustainability of the programme, and will be combined with a forthcoming study on the programme's impact on nutrition outcomes to generate evidence on cost-effectiveness.

| CONCLUSIONS
This study presents the financial and economic incremental costs of implementing an integrated agriculture-nutrition intervention through a micro-credit platform in Bangladesh. Cost-per beneficiary estimates compare favourably with multisectoral nutrition-sensitive interventions implemented through different platforms. These results demonstrate that a standardised approach for measuring the costs of multisectoral nutrition strategies enhances comparability and transparency, increasing the application of cost data for assessing affordability for use in evaluation, planning and policymaking.

AUTHOR CONTRIBUTIONS
Giang Thai led data collection and cost analyses and contributed to the manuscript. Amy Margolies contributed to the cost study methodology, conducted cost analyses and led drafting of the manuscript. Aulo Gelli contributed to the cost study methodology and to conception and design, provided technical advice, drafted and reviewed manuscripts. Nasrin Sultana co-led data collection and reviewed the manuscript. Esther Choo contributed to the data analysis and reviewed the manuscript. Neha Kumar reviewed the manuscript. Carol Levin contributed to the cost study methodology and to conception and design, provided technical advice, participated in data collection, edited the manuscript and reviewed manuscripts.